\(\mathsf {FABBOO}\)  is an online ensemble of sequential weak learners that tackles class imbalance and cumulative discriminatory outcomes in the stream. Moreover, \(\mathsf {FABBOO}\)  deals with concept drifts, through blind adaptation, by employing a base learner that is able to react to concept drifts. In particular, we employ Adaptive Hoeffding Trees (AHT) [1]} as weak learners; AHT is a decision-tree induction algorithm for streams that ensures DT model adaptation to the underlying data distribution by not only updating the tree with new instances from the stream, but also by replacing sub-trees when their performance decreases.
